A meta-optimized hybrid global and local algorithm for well placement optimization
A meta-optimized hybrid global and local algorithm for well placement optimization
复制标题
用于井位优化的元优化混合全局和局部算法
DOI：
10.1016/j.compchemeng.2018.06.013
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发表时间：
2018
影响因子：
4.3
通讯作者：
Liu Chen
中科院分区：
文献类型：
--
作者：
Chen Hongwei;Feng Qihong;Zhang Xianmin;Wang Sen;Ma Zhiyu;Zhou Wensheng;Liu Chen
井位优化是一项复杂而耗时的工作。一个高效、健壮的算法可以提高优化效率。在这项工作中，我们提出了一种元优化的混合猫群网格自适应直接搜索算法(O-CSMADS)用于井位优化。将猫群优化(CSO)算法、网格自适应直接搜索(MADS)算法和粒子群优化(PSO)元优化方法相结合，使O-CSMADS具有全局搜索能力和局部搜索能力。在三个不同的算例中，我们对O-CSMADS、混合猫群网格自适应直接搜索(CSMADS)算法、CSO算法和MADS算法的优化性能进行了详细的比较。结果表明，O-CSMADS算法的性能优于独立的CSO、MADS和CSMADS算法。此外，对于不同的问题，最优控制参数是不同的，这说明算法参数的优化是必要的。该方法对其他石油工程优化问题，如井型优化、布井与控制联合优化等也具有很大的应用潜力。
Well placement optimization is a complex and time-consuming task. An efficient and robust algorithm can improve the optimization efficiency. In this work, we propose a meta-optimized hybrid cat swarm mesh adaptive direct search (O-CSMADS) algorithm for well placement optimization. By coupling Cat Swarm Optimization (CSO) algorithm, Mesh Adaptive Direct Search (MADS) algorithm, and Particle Swarm Optimization (PSO) meta-optimization approach, O-CSMADS has global search ability and local search ability. We perform detailed comparisons of optimization performances between O-CSMADS, hybrid cat swarm mesh adaptive direct search (CSMADS) algorithm, CSO, and MADS in three different examples. Results show that O-CSMADS algorithm outperforms stand-alone CSO, MADS, and CSMADS. Besides, optimal controlling parameters are not same for different problems, which indicates that the optimization of algorithmic parameters is necessary. The proposed method also shows great potential for other petroleum engineering optimization problems, such as well type optimization and joint optimization of well placement and control.